What is data value?
Data is a precious business asset. But, as valuable as it is, enterprise data isn’t like tangible corporate assets with a quantifiable market value. Real estate, technical equipment, office furniture — they’re all easily readable line items on a budget. Even other intangible resources, such as personnel and IP, have established standards and protocols for valuation.
What makes data so different? Data assets can be moved, measured, and stored. Some datasets — such as marketing lists — can be bought and sold as raw data. But we don’t treat data as a tangible asset.
On the other hand, data can also be shared, replicated, and transformed. It pours in from every direction: web forms and social media, applications and IoT sensors, internal information systems and external data sources. Data is an endless resource, yet businesses happily invest millions in data management because they know it will be worth it. But how do they know whether an investment in data has returned a positive ROI?
In short, what do we mean when we talk about data value?
Data value definition
In computer science, “data” describes information that has been translated into letters, numbers, and/or symbols so that it can be read, moved and manipulated by a machine. Information that any person would represent as numbers, such as statistics, phone numbers, or inventory counts, are obviously data. But in a digital-first world like ours, almost anything can be data. The search terms you type in to answer an important question, the amount of time you spend in an app, even unstructured data like the content of your most recent LinkedIn post are raw data that has value to someone, somewhere.
But when we talk about data value in the business sense, we’re referring to the total, measurable financial impact of all the ways your organization applies enterprise data.
Across the data lifecycle, analytical processes make raw data useful by translating those attributes into intelligence with both context and a business purpose. Decision-ready data and analytics inform business activities. When those activities save money or make money for an organization, they’ve finally unlocked that data’s business value.
Measuring data value
Since the value of data is understood in terms of business impact, quantifying an organization's data value starts with its data strategy. As we’ve discussed, you can derive value from data by using it to reduce costs, increase revenues, or generate income. In any case, the way to uncover your data’s net value is by calculating your return on investment. Capturing, moving, preparing, and storing data is not free. In order to determine your ROI on data, you’ll need to measure its costs and benefits.
Investment in data and analytics is always an investment in the business. The impact of data investment, though, can vary wildly depending on where in the data lifecycle you invest in data. Back in 1992, George Labovitz and Yu Sang Chang proposed the 1-10-100 Rule to describe the impact of data costs:
- $1 to verify or standardize new data at the point of entry
- $10 if you wait to cleanse the data until it’s in your system
- $100 in damage control if data is used without being cleansed
To put it simply, it costs less to ensure quality inputs than it does to correct flawed outputs. This is especially true when, instead of being subject to analysis by a human expert who might have a chance to catch any errors, those outputs are used directly by apps and machine learning algorithms and could be having disastrous real time effects for hours or even days before the error is caught.
Return on data can’t be measured by data volume, data speed, or even data quality alone. To be valuable, data has to be accessible to the people who need it, when they need it — a state we call data health. An organization with healthy data has sufficient data agility to meet the changing demands of the business, a shared data culture throughout the organization, and data trust within and across the lines of business.
Business initiatives to maximize data value
There are many ways that data can bring value to an organization. Data can help a company establish its competitive advantage. Advanced analytics can improve decision-making across the organization. Careful application of customer data can expose potential new markets or revenue streams. The potential use cases of healthy data are effectively endless; but here are a few types of data-driven initiatives with measurable financial impact:
Improving operational efficiency
Shared, trusted data breaks down silos between different departments and provides real-time visibility into activity across the organization, with partners, or with vendors in your supply chain.
For example, manufacturers can cut maintenance costs and minimize downtime using real-time sensor data and predictive analytics. By better anticipating equipment failure, they can plan timely repair or replacement.
Boosting productivity through automation
From email vacation responders to robot vacuums, these days we're all automating something to make our lives easier. Automated processes save people tedious steps at work, improving employee productivity.
In the financial industry, banks and financial institutions automate the preparation of data entered by customers, reserving employees’ valuable time for customer service tasks that require human expertise.
Reaching new audiences
Gartner has found that 63% of marketers struggle with personalized marketing. Data opens the door to customer segmentation and analyses that really help you understand who your customers are.
Field marketers who integrate data from sources ranging from CRM to social media feeds gain a better understanding of customer moods, preferences, and media consumption and engagement habits. They use this to create personal, engaging experiences tailored to particular customer segments and markets.
Improving customer satisfaction
As operations scale up and customer communications flood in, a data-driven 360-degree view of the customer helps bring back a human touch.
Retailers integrate data from online and offline channels. Whenever customers visit in person, shop online, or call, they’ll get a cohesive customer experience that drives personal connection and loyalty.
Innovating through augmented research and development
Big data is a game-changer for R&D. Artificial intelligence is much better than we are at detecting the signal in the noise of vast amounts of data. Machine learning (ML) can make new discoveries using historical data, or deliver real-time insights.
Consumer apps analyze user behavior to build product intelligence. That information inspires new features and informs go-to-market strategy for product updates and new products.
In addition to using data to fuel revenue-driving activities from sales to product innovation, organizations can use data to generate value directly. Gartner describes data sharing as a key activity that’s increasingly necessary to succeed in the Big Data world. With the right approach, innovative companies have been able to turn their competitors into paying customers. They do this by packaging prepared data itself as a product or service.
Monetizing data as a service (DaaS) is easier said than done. Delivering data to external customers raises the bar for data quality analysis and data availability. While any organization should already aim for infrastructure that provides internal customers with ready access to highly trusted data, making data externally available can reveal flaws. Issues that a company may have been willing to put up with for internal users could be deal breakers for data monetization.
Before starting to share data externally, it’s also important to think carefully about the repositories where data is stored and managed. You may not want to give data customers access to a data warehouse where all your corporate data is stored. Instead, it would be wise to partition the monetized data, for example into a data mart. That way, the data for sale is safely isolated away from sensitive or proprietary data you don’t intend to share.
Data value in action: Case studies
AstraZeneca — Turning time into profit
Biopharmaceutical giant AstraZeneca focuses on the discovery, development, and commercialization of prescription medicines. The company operates in over 100 countries and its innovative medicines are used by millions of patients worldwide.
With a data catalog powered by Talend Data Fabric, 90 percent of the life science company’s data can now be ready for analysis within 3 minutes. This has shaved a month off the time it takes AstraZeneca to run clinical trials, saving $1 billion per year. By making data more accessible across the company, they literally made their data more valuable to the business. “For every dollar we spend on a data initiative, we are able to get $40 in return,” says Andy McPhee, Science and Enabling Units Data & Analytics Engineering Lead at AstraZeneca.
Covanta — Improving ROI through operational efficiency
Covanta provides sustainable waste to energy and environmental solutions across its 41 modern and clean Waste to Energy (WtE) facilities in North America and Europe. Every year, Covanta safely converts approximately 21 million tons of waste into clean, renewable electricity to power over 1 million homes and recycles 500,000 tons of metal.
Covanta believes in applying a holistic approach to data to fuel the business and meet organizational objectives. This requires both a data use strategy, which identifies business objectives and quantifies goals, and a data management strategy, which specifies how the data is used.
The Covanta team can now optimize operational efficiency to ensure the safety of workers while generating clean electricity and maintaining maximum uptime. This has resulted in at least 10% savings per year for maintenance activities alone.
How do I uncover data value?
A solid foundation for data management helps everyone — from IT professionals to line-of-business leaders — get more value out of data. Talend provides a unified, end-to-end solution that delivers accurate, reliable data quickly and easily. Unlike hand-coding or cobbled-together approaches that tax productivity, Talend offers a proven, enterprise-grade solution that excels in multi-cloud and hybrid environments.
Do you have the metrics you need to assess data value at your organization? Would you like to improve your data ROI? Talend can help. Talend is on a mission to deliver the metrics necessary for organizations to measure data health. Diagnosing problems that make data harder to use at your organization could be the first step to improving your data’s value. Contact us today to get started.
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